Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning

Researchers found that fine-tuning large language models with explanations attached to labels significantly improves classification accuracy compared to label-only training. Surprisingly, even random token sequences that mimic explanation structure provide similar benefits, suggesting the improvement comes from increased token budget and regularization rather than semantic meaning.